Computer-aided classification of anomalies in anatomical structures

ABSTRACT

Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly&#39;s neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of Summers et al., co-pending U.S.patent application Ser. No. 11/775,011, filed Jul. 9, 2007, which is adivisional of Summers et al., U.S. patent application Ser. No.10/671,749, filed Sep. 26, 2003, now U.S. Pat. No. 7,260,250, whichclaims the benefit of U.S. Provisional Patent Application No.60/415,308, filed Sep. 30, 2002, by Summers et al., entitled“COMPUTER-AIDED CLASSIFICATION OF ANOMALIES IN ANATOMICAL STRUCTURES,”all of which is are hereby incorporated herein by reference.

FIELD

The field relates to automated analysis of digital representations ofanatomical structures.

BACKGROUND

Due to the availability of technology for non-invasive observation ofsoft tissues of the human body, significant advances have been made inthe field of medicine. For example, a number of machines now make itpossible to routinely observe anatomical structures such as the heart,colon, bronchus, and esophagus.

Due to widespread availability of skilled technicians and reduction incost of the necessary equipment, non-invasive observations can now beemployed as a part of routine preventive medicine via periodicexaminations. The availability of such non-invasive capabilities bothreduces the risk of observation-related injury or complication andreduces discomfort and inconvenience for the observed patient. As aresult, patients tend to allow observation to be done more frequently,and medical conditions requiring attention can be detected early. Forexample, anomalies in an anatomical structure can be identified anddiagnosed at an early stage, when treatment is more likely to besuccessful.

In one popular observation technique called “Computed TomographyImaging” (“CT Scan”), multiple two-dimensional image slices are taken ofa particular section of the patient's body. A physician can then analyzethe slices to detect any anomalies within the observed section. Out ofthe anomalies found, the physician can judge which are anomalies ofinterest requiring further attention or treatment.

To assure adequate coverage of the section being observed, a largenumber of slices can be obtained to increase the observation resolution.However, as the number of slices increases, the amount of data presentedto the physician becomes overwhelming. Accordingly, various softwaretechnologies have been applied with some success to aid the physician inanalyzing the data to find anomalies.

Although progress has been made in employing software to assist indetection of anomalies in anatomical structures, there are significantlimitations to the current techniques. For example, one problemconsistently plaguing such systems is the overabundance of falsepositives.

Typically, the software approach correctly identifies anomalies ofinterest (i.e., the software exhibits superior sensitivity). However,the software also tends to incorrectly identify too many anomalies asanomalies of interest (i.e., the software exhibits poor selectivity). Ananomaly incorrectly identified as an anomaly of interest is sometimescalled a “false positive.”

False positives are troublesome because any identified positives must beconsidered and evaluated by a human classifier (e.g., the physician).Even if the physician can quickly dismiss an anomaly as a falsepositive, too many false positives consume an inordinate amount of timeand limit the usefulness of the software-based approach.

There thus remains a need for a way to improve the computer-basedapproaches for identifying anomalies of interest in anatomicalstructures. For example, selectivity can be improved.

SUMMARY

Embodiments described herein include methods and systems for analyzing adigital representation of an anatomical structure to classify anomaliesin the anatomical structure. For example, an anomaly can be classifiedas of interest (e.g., a likely polyp or lesion requiring furtherevaluation and consideration) or not of interest (e.g., an anomaly suchas a false positive or a misidentified normal feature that does notrequire further evaluation).

In some embodiments, a set of one or more candidate anomalies isprocessed via a number of techniques to collect various characteristicsof the anomalies. A software classifier can use the characteristics toclassify an anomaly in the set (e.g., as of interest or not ofinterest).

In some embodiments, the neck of an anomaly can be identified. The neckcan then be analyzed to collect a set of one or more neckcharacteristics. Based on the neck characteristics, a softwareclassifier can make a more accurate classification.

In some embodiments, the wall thickness associated with an anomaly canbe analyzed and normalized. Based on normalized wall thickness, asoftware classifier can make a more accurate classification.

In some embodiments, template matching can be performed to determinewhether the anomaly matches one or more templates. Again, the templatematching characteristics can be used by a software classifier for moreaccurate classification of a candidate anomaly. Template matching canalso be used to eliminate certain areas from consideration.

Any of the above characteristics can be used as input to a classifier,such as a rule-based system, a neural network, or a support vectormachine. The classifier can draw upon the various characteristics toprovide a classification of the candidate anomalies (e.g., as being ofinterest or not being of interest).

The technologies can be applied to any of a variety of anatomicalstructures, such as the colon, bronchus, blood vessels, bladder, urinarytract, billiary tract, cerebrospinal spinal fluid containing spaces ofthe brain, paranasal sinuses, chambers of the heart, or the like.

Additional features and advantages of the invention will be madeapparent from the following detailed description of illustratedembodiments, which proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart showing an exemplary method for detectinganomalies of interest in an anatomical structure.

FIG. 2 is a flowchart showing an exemplary method for analyzing arepresentation of an anatomical structure to detect anomalies ofinterest in the anatomical structure.

FIG. 3 depicts an exemplary candidate anomaly of interest and the neckassociated therewith.

FIG. 4 depicts an exemplary candidate anomaly of interest and wallthickness associated therewith.

FIG. 5 is a flowchart showing an exemplary method for measuringcharacteristics associated with an anomaly neck.

FIG. 6 is a flowchart showing an exemplary method for identifying ananomaly neck.

FIGS. 7A-7D are views of exemplary candidate anomalies having exemplarycurvatures.

FIG. 8 is a flowchart showing an exemplary method for measuring wallthickness.

FIG. 9 is a graph showing an exemplary intensity profile.

FIGS. 10 and 11 are a flowchart showing an exemplary method forcalculating wall thickness.

FIGS. 12A-12D are graphs of exemplary intensity profiles used tocalculate wall thickness.

FIGS. 13A-13C are further graphs of exemplary intensity profiles used tocalculate wall thickness.

FIG. 14 is a flowchart of an exemplary method for implementing templatematching to classify a candidate anomaly.

FIGS. 15, 16, and 17 are views of exemplary templates for templatematching techniques.

FIG. 18 is a view of an exemplary template for rectal tube removal.

FIG. 19 is a view of a graphical depiction presented for identificationof anomalies of interest.

FIG. 20 is a block diagram of an exemplary computer system forimplementing the described technologies.

DETAILED DESCRIPTION Definitions

An anomaly of interest includes any abnormal feature occurring in ananatomical structure. Anomalies can include surface anomalies (e.g.,wall surface anomalies). For example, anomalies of interest include anynumber of cancerous or pre-cancerous growths, lesions, polyps, or otherfeatures. In a fully automated system, the location or image of ananomaly of interest can be provided as a result. In a system with user(e.g., physician) assistance, the anomaly of interest can be presentedto the user for confirmation or rejection of the anomaly as being ofinterest. Those anomalies confirmed as of interest can then be providedas a result.

Classifying includes designating an anomaly as of interest ordesignating an anomaly as not of interest (e.g., disqualifying acandidate anomaly as being of interest). For example, in the case of avirtual colon, an anomaly can be classified as a polyp. In the exampleof a virtual bronchus, an anomaly can be classified as a lesion.

A representation of an anatomical structure includes any digitalrepresentation of an anatomical structure (or portion thereof) storedfor processing in a digital computer. For example, representations caninclude two- or three-dimensional representations of an anatomicalstructure stored as an image via a variety of data structures.Representations can be composed of pixels, voxels, or other elements.Such a representation is sometimes called “virtual” (e.g., a “virtualcolon”) because it is a digital representation that can be analyzed tolearn about the represented anatomical structure.

Overview of Technologies for Detecting Anomalies of Interest

An overview of an exemplary method 100 for detecting anomalies ofinterest in an anatomical structure is shown in FIG. 1. The actionsshown in the method 100 can be performed in computer software.

At 112, a representation of the anatomical structure is acquired. Forexample, a CT scan can be taken of a human patient to acquire imageslices of the patient's colon. Various preprocessing can be performed onthe representation as desired. For example, the slices can be combinedto form a three-dimensional image.

At 122, the representation is analyzed to detect anomalies of interest.For example, in the case of a virtual colon, polyps can be detected.

At 132, the results are provided. For example, sites where anomalies ofinterest appear can be presented for consideration by a physician.

Overview of Representations of Anatomical Structures

Acquisition of a representation of an anatomical structure is typicallydone by performing a scan of the soft tissues of the patient. Forexample, a CT scan can be performed according to any number of standardprotocols. CT scans can be used to generate thin-section CR data (e.g.,helical scan CT data). The representation can be analyzed immediatelyafter the scan, or the representation can be stored for later retrievaland analysis. Exemplary techniques for acquiring scans are described inVining et al., “Virtual Colonoscopy,” Radiology 193(P):446 (1994),Vining et al., “Virtual Bronchoscopy,” Radiology 193(P):261 (1994), andVining et al., “Virtual bronchoscopy. Relationships of virtual realityendobronchial simulations to actual bronchoscopic findings” Chest109(2): 549-553 (February 1996), all of which are hereby incorporatedherein by reference.

Any number of hardware implementations can be used to acquire arepresentation of an anatomical structure. For example, the GE Advantage(e.g., high speed) scanner of GE Medical Systems, Milwaukee, Wis. can beused.

Overview of Analyzing a Representation of an Anatomical Structure toDetect Candidate Anomalies of Interest

An overview of an exemplary method 200 for analyzing a representation ofan anatomical structure to detect anomalies of interest in theanatomical structure is shown in FIG. 2. The actions shown in the method200 can be performed in computer software.

At 212, candidate anomalies of interest are detected in therepresentation of the anatomical structure. For example, curvaturesrelated to the structure can be computed and portions of the structureexhibiting a particular curvature type can be designated as a candidateanomaly of interest.

At 222, one or more characteristics of the detected candidate anomaliesof interest are calculated. For example, size, shape, or any number ofother characteristics may be relevant.

At 232, based on the characteristics of the candidate anomalies, thecandidate anomalies are classified. For example, the candidate can beclassified as “of interest” or “not of interest.” Sometimes suchclassification is described as “eliminating false positives.”Classification can be done in many ways. For example, a candidateanomaly of interest may be designated as “of interest” only if it meetscertain criteria or is so classified by a software classifier. Or, thecandidate anomalies can be ranked according to a scoring system.

Exemplary Characteristics of Candidate Anomalies of Interest

Exemplary characteristics of candidate anomalies of interest includethose related to the anomaly's neck, normalized wall thicknessassociated with the anomaly, and template matching. Given arepresentation of an anatomical structure or a region of suchrepresentation associated with a candidate anomaly of interest, softwarecan calculate any number of these or other characteristics for use inclassification of the candidate anomaly of interest.

Overview of Anomaly Neck Characteristics

FIG. 3 shows a portion 300 of a representation of an anatomicalstructure that includes an exemplary anomaly 312 and a neck 322associated therewith. Candidate anomalies of interest can be processedby software to first identify the neck 322 and then measure variouscharacteristics of the neck.

The neck 322 can be identified in any of a number of ways, such asfinding areas of hyperbolic curvature associated with the candidateanomaly of interest 312 or using deformable models. Characteristics tobe measured can include size (e.g., height, circumference, area enclosedby the neck), angle, orientation, irregularity, curvature, or relationof neck to adjacent haustral folds, if applicable.

Identification of the neck 322 (or failure to identify the neck 322) andany of the neck characteristics can then be used to advantage duringclassification of the candidate anomaly of interest 312.

Overview of Automated Wall Thickness Measurement

A variety of automated techniques can be used to measure wall thicknessassociated with an anomaly. One such technique is described in Vining etal., U.S. Pat. No. 5,920,319, filed Jul. 6, 1999, entitled “Automaticanalysis in virtual endoscopy,” which is hereby incorporated byreference herein. However, a number of issues arise when calculatingwall thickness.

FIG. 4 shows a portion 400 of a representation of an anatomicalstructure including a candidate anomaly of interest 412 which is locatedon a wall 422 of the anatomical structure. It may be desirable tomeasure wall thickness at a variety of locations (e.g., 422, 432A, 432B,and 432C), including the wall thickness at the anomaly 412 itself.

One way to measure wall thickness is to extend a normal 442 to anapproximating surface of the inner edge 452 of the wall. Voxel intensityalong the normal can be measured to identify the location of the inneredge 452 of the wall and the outer edge 454 of the wall, from which wallthickness can be calculated.

At the outset, it should be noted that constructing the normal 442 canbe challenging. Various techniques (e.g., gradients and patch fitting)can be used to assist in constructing the normal 442.

Further, identifying the location of the inner edge 452 of the wall orthe outer edge 454 of the wall within the digital representation 400 canbe problematic due to blending with adjacent structures and othertissues. Due to the environment in which the anatomical structureresides (e.g., the human body), the wall boundaries are often not clear.A number of rules can be constructed to assist in accurately finding thelocations so wall thickness can be properly calculated, and initialassessments may need to be corrected.

Overview of Automated Wall Thickness Normalization

One technique of assessing wall thickness is to normalize the thickness.For example, a plurality of wall thickness measurements (e.g., using thetechniques described herein) at a plurality of sites regional (e.g.,proximate) to a particular wall site can be made. The normalized wallthickness can then be calculated according to a formula, such as (1):

$\begin{matrix}\frac{W_{P} - W_{R}}{\sigma_{R}} & (1)\end{matrix}$where W_(P) is wall thickness at a particular site, W_(R) is regionalwall thickness, and σ_(R) is the standard deviation of the regional wallthickness values.

Various techniques for finding regional wall thickness can be used. Forexample, an annulus around a candidate anomaly of interest can be used.

The normalized wall thickness measurements associated with a candidateanomaly of interest can then be fed to a classifier by which thecandidate anomaly of interest is classified. An atlas of normal wallthicknesses can be used to further advantage (e.g., based on gender,age, or body habitus).

The technique of wall thickness normalization can also be useful whendepicting the representation of the anatomical structure forconsideration by a human observer (e.g., to a physician). For example,regions proximate to an identified anomaly or the anomaly itself can bedepicted in color(s) based on the normalized wall thickness of theregion to assist in drawing the physician's eye to regions having apotential anomaly of interest.

Overview of Automated Template Matching

Another measurement useful for classification can be performed viatemplate matching. Generally, a template can be developed that sharesshape characteristics with known anomalies of interest. A candidateanomaly of interest can then be compared to the template to obtain ameasurement.

Templates can be two- or three-dimensional and typically are capable ofbeing matched against a candidate anomaly of interest to determine howthe anomaly is to be classified. The digital template can be shifted,rotated, or sized as appropriate depending on circumstances. Nonrigidtemplate matching can be acquired including other transformations (e.g.,affine and non-affine) so that other shapes of interest can be detected.

As a byproduct of template matching, other useful measurements can becollected, such as finding the center of a candidate anomaly ofinterest, the radius of the anomaly, the orientation of the anomaly, andthe cross-sectional area or volume of the anomaly.

Another useful scenario to which template matching can be applied is toeliminate from consideration regions of the digital representation knownnot to be of interest. For example, in a colonography scenario, atemplate of the rectal tube can be used to eliminate the rectal tubefrom consideration.

Overview of Automated Classification of Candidate Anomalies of Interest

Any of the characteristics described herein can be measured and used forclassifying candidate anomalies of interest via a software classifier. Avariety of thresholds can be set based on observed performance of theclassifier.

The software classifier can take the form of a rule-based system, aneural network, or a support vector machine. The classifier can drawupon the various characteristics described herein to classify thecandidate anomalies. Specialized classifiers can be implemented toclassify anomalies within a particular anatomical structure (e.g.,colon, bronchus, and the like).

Overview of Presenting Results

Candidate anomalies of interest confirmed as being of interest can beprovided in a number of ways. For example, they can be presented in adigital gallery of images. Color coding can be used to indicate whichregion of an anomaly was determined to be the anomaly neck.

Details of Exemplary Implementation Detecting Candidate Anomalies ofInterest

Any number of techniques can be used to analyze a representation of ananatomical structure to detect candidate anomalies of interest. One suchtechnique uses curvature and is described in U.S. Pat. No. 6,246,784 toSummers et al., filed Aug. 18, 1998, and entitled “Method for SegmentingMedical Images and Detecting Surface Anomalies in AnatomicalStructures,” which is hereby incorporated herein by reference. Any otherapproach capable of detecting anomalies in a representation of ananatomical structure can be used as an alternative.

The set of anomalies detected by such an approach can be consideredcandidate anomalies of interest for any of the technologies describedherein. Characteristics of the candidates can then be measured asdescribed below and used to classify the candidates (e.g., as “ofinterest” or “not of interest”).

Details of Exemplary Implementation Anomaly Neck

An overview of an exemplary method 500 for measuring characteristicsassociated with an anomaly neck is shown in FIG. 5. The method can beperformed in software. At 512, the anomaly neck is identified. Forexample, the area proximate an anomaly can be searched to find locationsdesignated as part of the neck. At 522, one or more characteristics ofthe identified neck are measured.

An exemplary method 600 for identifying a neck, such as described in 512above is shown in FIG. 6. The method can be performed by software.

At 612, the location of an anomaly is established. For example, theboundary of a region designated by an anomaly detector can beestablished. In the example of an anomaly detected by identifyingelliptical curvature of the peak type, the boundary of the regionexhibiting elliptical curvature of the peak type can be used. Forexample, a closed loop around the region can be drawn by software. Or, aregion (e.g., center point) within such curvature can be used.

At 622, the region is expanded. For example, a series of subsequentloops (e.g., while preserving continuity by the loop) can be formedfurther away from the original region (e.g., the region havingelliptical curvature of the peak type).

At 632, a test is made to see if the region has been sufficientlyexpanded. For example, a pre-determined maximum distance can be used.Instead of using Euclidean distance, the distance can be measured alongthe surface (e.g., the smallest distance between a point of a loopdefining the region and any point on the boundary of the originalregion). If the region has not been sufficiently expanded, expansioncontinues at 622.

After the region has been sufficiently expanded, the neck is identifiedwithin the region at 642. The identification need not be a separateaction as shown; it can be done in concert with the earlier actions(e.g., while expanding the region). Instead of using the loop processdescribed, deformable models can be used.

To identify the neck, curvature criteria can be used. For example, thosepoints on the loops described above having hyperbolic curvature (e.g., asaddle point) within a pre-designated range can be included in theanomaly neck.

Having identified the neck, one or more characteristics of the neck canbe measured. Exemplary neck characteristics include the following: necksize, neck height, circumference of neck, area enclosed by neck, angleof neck, orientation of neck, irregularity of neck, curvature of neck,and relation of neck to adjacent haustral folds (e.g., whether abuttinga fold or on a fold).

As a by-product of measuring certain characteristics of the anomalyneck, various other characteristics related to the anomaly can bemeasured. For example, when determining the orientation of the neck, aplane can be fit to the circumference of the neck. Fitting a plane tothe circumference of the neck can then be used to determine the base ofthe anomaly for purposes of measuring the depth of protrusion of theanomaly or the anomaly height.

Irregularity of the neck can be measured. For example, a fractaltechnique or some other measure of jaggedness can be used.

Curvature assessment can take a variety of forms. For example, Gaussiancurvature, mean curvature, principal curvature, shape index, andcurvedness can be derived by processing curvature values. In a scenarioinvolving a three-dimensional representation, such measurements can becalculated using partial derivatives, finite differences, convolutionkernels, or by fitting of approximating curves or surfaces. For example,if a neck angle for an anomaly is acute, the maximum principal curvaturemay be large, and such a measurement can be used to distinguish a truepositive from a false positive.

The above techniques can also be used in scenarios involving atwo-dimensional representation of an anatomical structure. In suchscenarios, the anomaly, adjacent wall, or interface with a lumen of theanatomical structure can be segmented (e.g., via an edge detectionfilter). Then, the angle made by the junction of the fleshy portion ofthe anomaly and the inner edge of the wall of the anatomical structurecan be measured. Alternatively, or in addition, isosurface and level settechniques can be used to identify the desired edge of the anomaly andthe inner anatomical structure wall.

Measurement can also or alternatively be performed at locations otherthan the junction described above. For example, measurement can be madewithin the junction, adjacent to the junction, within the anomaly, or onthe anatomical structure wall, along an outer edge of the wall, or inclose proximity to the wall or the anomaly.

The minimum principal curvature can also be used to assess anomaly size.Also, by fitting the anomaly base to a shape (e.g., an ellipse), theeccentricity or aspect ratio can be measured, which can be useful fordistinguishing anomalies of interest from folds in the anatomicalstructure.

Any of the above neck or other measurements can be used to classify orassist in classifying a candidate anomaly (e.g., with any of theclassifiers described herein). For example, in the case of a virtualcolon, the neck or other measurements can be used to classify or assistin classifying an anomaly as a polyp.

Exemplary Implementation Maximum Principal Curvature of Polyp Neck

A set of over 24,000 anomalies known to be false positives and 38anomalies known to be true positives in colon-related data were analyzedby measuring the maximum principal of the anomaly (e.g., polyp) neck.The area under the ROC curve for the average value of the maximumprincipal curvature of the neck was 0.67.

Details of Exemplary Implementation Exemplary Curvatures

Views of exemplary candidate anomalies of interest are shown in FIGS.7A-7D. A side view of a candidate anomaly of interest 710 is shown inFIG. 7A. Some regions (e.g., surfaces) 712 and 714 of the anomaly 710have elliptical curvature of the peak type, and such ellipticalcurvature can be used by the software to identify the candidate anomalywhen processing a digital representation of an anatomical structure.Other regions (e.g., surfaces) 716 and 718 have hyperbolic curvature andare thus identified as being part of the anomaly neck.

A top view of the candidate anomaly of interest 710 is shown in FIG. 7B.Again, the same regions (e.g., surfaces) 712 and 714 of the anomaly 710have elliptical curvature of the peak type, and such ellipticalcurvature can be used by the software to identify the candidate anomaly.Other same regions (e.g., surfaces) 716A, 716B, 718A, and 718B havehyperbolic curvature and are thus identified as being part of theanomaly neck. In the example, the candidate 710 was classified as ananomaly of interest (e.g., a polyp or “true positive”).

A side view of a candidate anomaly of interest 730 is shown in FIG. 7C.Some regions (e.g., surfaces) 732 and 734 of the anomaly 710 haveelliptical curvature of the peak type, and the software can identifysuch regions to identify the candidate anomaly. Other regions (e.g.,surfaces) 736 and 738 have hyperbolic curvature and are thus identifiedas being part of the anomaly neck.

A top view of the candidate anomaly of interest 730 is shown in FIG. 7D.The same regions (e.g., surfaces) 732 and 734 of the anomaly 730 haveelliptical curvature of the peak type, and the software can identifysuch regions to identify the region as a candidate anomaly. The othersame regions (e.g., surfaces) 736 and 738 of the anomaly 730 havehyperbolic curvature and are thus identified as being part of a neck ofthe anomaly 730. In the example, the candidate 730 was classified as ananomaly not of interest (e.g., a “false positive”).

The candidate 710 is classified as an anomaly of interest because thecurvature of the neck (e.g., regions 716, 716A, 726B, 718, 718A, and718B) is greater than that of 730 (e.g., regions 736 and 738). Forexample, the region 736 is less highly curved than the region 718.Although the example in FIG. 7A shows that the head of the candidate 710is larger than the diameter of the neck, such is not necessary forclassification as an anomaly of interest. For example, a key attributefor classification can be curvature of the neck.

Details of Exemplary Implementation Measuring Wall Thickness

The ability to accurately measure wall thickness can be beneficial forvarious of the techniques described herein. Such measurements are oftenchallenging because other structures or features in the digitalrepresentation of an anatomical structure make it difficult to determinethe orientation of the wall and the location of the inner and outerportions (e.g., boundaries) of the wall.

One possible way to measure wall thickness is to draw a normal to thewall and then evaluate the intensity of voxels or pixels along the wall.An exemplary method 800 for measuring wall thickness is shown in FIG. 8.The method 800 can be performed by software.

At 812, a normal to the wall being measured is calculated. For example,given a point, a ray is drawn through the wall perpendicular to the wall(e.g., perpendicular to the wall surface).

At 822, an intensity profile is generated. For example, voxelintensities along the normal can be measured.

At 842, the intensity profile is evaluated to determine wall thickness.Because the initial point from which the normal is drawn may be in avoid (e.g., air 460 of FIG. 4) or within the anatomical structure walls(e.g., between the inner edge 452 of the wall and the outer edge 454 ofthe wall of FIG. 4), analysis of the intensity profile can be done toallow for such scenarios as well as other scenarios.

An exemplary intensity profile 900 is shown in FIG. 9. The x-axisrepresents distance away from the starting point (e.g., point 470 ofFIG. 4) along the normal, and the y-axis represents the voxel intensityat a point on the normal at such a distance. Typically, a higherintensity indicates higher density within the anatomical structure. So,for example, a very low intensity typically indicates a void or air.Using various thresholds and rules, a point 921 on the profilerepresenting the inner edge of the wall can be calculated, and a point922 on the profile representing the outer edge of the wall can becalculated. The wall thickness can then be calculated by computing thedistance 932 between the two points. The region 940 represents a regionof the digital representation beyond (e.g., outside) the wall. Forexample, the region 940 might indicate another portion of the anatomicalstructure if the structure winds (e.g., folds) onto itself.

With reference to FIG. 4, gradients can be computed to determine thenormal 442. For example, average gradients over small regions can becomputed. Alternatively, a computed center line 462 can be computed fromwhich the normal 442 can be generated by measuring perpendicular lines.

Other techniques, such as fitting surface patches (e.g., b-spline orother approximating patches) to the inner edge 452 of the wall or theouter edge 454 of the wall, the anomaly 412 or within close proximitythereto can be used. Differentiation, cross products of vectors lyingwithin a plane tangent to the point of interest, or other mathematicalmethods can then be used to compute the normal 442.

Having calculated the normal 442, the location of the inner edge 452 ofthe wall (e.g. an inner surface) can be identified. The precise locationof the inner edge of the wall is sometimes uncertain due to volumeaveraging artifact and blending of adjacent structures within a voxel.Such adjacent structures may be large relative to the thickness of thewall. Various rules can be used to evaluate the intensity profile.

For example, in situations where the voxel intensity along the ray(e.g., from the point 442) reaches that of water or soft tissue density(e.g., 0 HU to 60 HU), the inner edge of the wall of the anatomicalstructure can be set at a point midway between that of air (−1,000 to−1,024 HU) and soft tissue (0 to 60 HU). However, when voxel intensitiesalong the ray do not reach soft tissue intensity and when volumeaveraging artifact is suspected, the location of the inner edge of thewall can be set midway between that of air and the peak voxel intensityalong the ray. In both of these situations, an upper limit on the raydistance is used to avoid straying far from the expected location of theouter edge of the wall of the colon. For example, upper limits of one ortwo centimeters may be useful.

Then, the outer edge of the wall can be located, which may involveadditional heuristics. When the outer edge of the wall of the anatomicalstructure (e.g., a colon) abuts fat, the location of the outer edge ofthe wall may be set to the midpoint between soft tissue attenuation andfat (−100 HU). However, due to volume averaging artifact, the wall maybe so thin as to never reach soft tissue attenuation. In such ascenario, voxel intensities along the ray may not exceed fatattenuation. If so, the outer edge of the wall can be set to the firstfat voxel. In cases of volume averaging artifact where even fatattenuation levels are not reached, the location of the outer edge ofthe wall may be set to the location of the peak voxel intensity.Locations and voxel intensities can be computed using floating pointnumbers and trilinear or other interpolation methods in order to improveaccuracy.

Details of Exemplary Implementation Rules for Measuring Wall Thickness

An exemplary detailed method 1000 for calculating wall thickness from anintensity profile is shown in FIGS. 10 and 11 in the functionWall_Calc(in: C0, Dir; out: C2, WT2). The method 1000 can be implementedin software via any number of programming languages.

On a general level, the method can analyze voxel intensities along anormal to the wall with reference to thresholds (e.g., the “Inner_wall,”“Low_fat,” and “High_fat” thresholds) to determine wall thickness.“Low_fat” indicates a lowest voxel intensity consistent with fat.“High_fat” indicates a highest voxel intensity consistent with fat,taking into account volume averaging with soft tissue (e.g., assumingsoft tissue is 20 HU). Undulations of intensities can be handled incases, for example, where the intensity profile vacillates between orwithin the Low_fat and High_fat regions.

The rules can take a point at an arbitrary location with respect to thesurface of the wall and determine the wall thickness corresponding tothe point. In some cases, the rules “backtrack” along the normal.

In the example, the function takes “C0” and “Dir” as input and produces“C2” and “WT2” as output. The function also relies on the digitalrepresentation of the anatomical structure. Intensities of voxels withinthe representation are acquired via the function “Inten(<voxel>).”Thresholds for “Inner_wall,” “Low_fat,” and “High_fat” are also reliedupon. In the example, these are constants (e.g., −500, −100, and −40HU); however, any number of other choices for these values can besimilarly successful, and the technology is not limited to one set ofvalues. If necessary, the values may be adjusted or calibrated based onthe relative grayscale levels of scans.

“C0” is the point at which wall thickness is to be measured (e.g., thepoint 470 of FIG. 4 and is possibly the predicted or identified locationof a candidate anomaly), and “Dir” is the vector (e.g., related to theray 442) normal to the wall.

“C2” is sometimes called the corrected version of “C0” and is placedwithin the wall (e.g., at 480) to designate the position of the anomaly.“C0” may happen to be floating in air or embedded between the inner edgeof the wall and the outer edge of the wall. WT2 is the measured wallthickness.

At 1002, voxel intensity at the point C0 is determined and stored as J0.Then, at 1004, it is determined if the intensity is less than the“Inner_wall” threshold. If so, the method continues at 1012, where asearch is performed along the ray Dir for the maximum intensity; themaximum intensity is stored as “Jmax,” and the corresponding pointlocation is stored as “Cmax.” At 1014, if Jmax is less than the“Inner_wall” threshold, at 1016 the corrected point C2 is set to Cmax,and the wall thickness WT2 is set to zero (0).

If, at 1014, Jmax is not less than the “Inner_wall” threshold, at 1022 asearch is made backward (e.g., back to the original C0 point) until thevoxel intensity is or passes that of the “Inner_wall” threshold. Forexample, searching can be done until the intensity equals or is lessthan the threshold. The location of the point is stored as “Cinner.” At1024, the wall thickness WT2 is set to the absolute value ofCmax-Cinner, and corrected point C2 is set to Cinner+0.5*WT2*Dir.

Returning to 1004, if J0 is not less than the threshold set for“Inner_wall,” at 1032, it is determined whether J0 is greater than thethreshold for “Low_fat.” If so, at 1042, a backward search along the ray“Dir” is done until the intensity is equal to or passes the “Inner_wall”threshold; the location of such a voxel is stored as “Cinner.” Then, at1044, a forward search (i.e., away from the original point “C0”) isperformed along the ray “Dir” until the intensity is equal to or passesthe “Low_fat” threshold; the location of such a voxel is stored as“Couter.”

Then, at 1046, it is determined whether the next checking point (e.g.,the point adjacent “Couter”) has an intensity greater than the thresholdfor High_fat. If so, at 1056, a forward search is made until theintensity is equal to or passes High_fat. Such a point is stored as“Couter.”

Otherwise, at 1058, the wall thickness “WT2” is set equal to theabsolute value of “Couter”−“Cinner” and the corrected point “C2” is setto “Cinner”+0.5*“WT2”*“Dir.”

Returning now to 1032, if “J0” is not greater than the threshold set for“Low_fat,” at 1062, a forward search along the ray “Dir” is made for themaximum intensity voxel. The location is stored as “Cmax,” and theintensity is stored as “Jmax.”At 1062, if Jmax is greater than thethreshold set for “High_fat,” at 1066, a forward search is made alongthe ray “Dir” until the intensity is equal to or passes the thresholdset for “High_fat.” The location of such a point is stored as “Couter.”

Then, at 1068, a backward search is performed along the ray defined by“Dir” until the intensity is equal to or passes the “Inner_wall”threshold. The location of such a point is stored as “Cinner.” Then,1058 is performed as described above.

Returning to 1064, if “Jmax” is not greater than the “High_fat”threshold, at 1072, it is determined whether “Jmax” is greater than“Low_fat.” If so, at 1074, a backward search is done along the ray “Dir”until the intensity is equal to or passes the “Inner_wall” threshold.The location of such a point is stored as “Cinner.” At 1076, a forwardsearch is then done until the intensity is equal to or passes the“Low_fat” threshold. The location of such a point is stored as “Couter.”Then, 1058 is performed as described above.

Returning to 1072, if it is determined that Jmax is not greater than the“Low_fat” threshold, at 1082, a backward search is performed until apoint with intensity equal to or passing the “Inner_wall” threshold isfound. The location of such a point is stored at “Cinner.” Then, at1084, the value for “Couter” is set equal to the value of “Cmax”computed in 1062. Then, 1058 is performed as described above.

Similar results can be achieved via different approaches, and variations(e.g., using thresholds other than the exemplary ones presented) can beemployed.

Details of Exemplary Implementation Analysis of Exemplary IntensityProfiles

Exemplary Intensity Profiles are shown in FIGS. 12A-12D and FIGS.13A-13C. In the examples, the x-axis represents a location along anormal to the wall (e.g., along a ray), and the y-axis represents voxelintensity at the location. The exemplary thresholds “High_fat,”“Low_fat,” and “Inner_wall” are also shown. C0 indicates the originalpoint (e.g., for the ray generated normal to the wall). Wall thickness“WT2” is also shown (if any), and C2 indicates the location of theanomaly (e.g., because C0 was provided as the initial location of theanomaly, C2 is sometimes called the “corrected location of C0”).

The corrected position can be calculated via the following formula:Corrected_(—) pos=Old_(—) pos+c2*Ray  (2)where Old_pos, Ray (e.g., “Dir”), and Corrected_pos are 3-dimensionalvectors, and C2 is a scalar (number).

The intensity profiles can be processed according to the rules describedabove to generate the wall thickness and corrected anomaly locations. Inthe example of 12A (“Scenario I”), at the portion 1212, there are atleast two subsequent points within fat (e.g., between high fat and lowfat). The location 1216 is stored and thereby designated as the inneredge of the wall (e.g., “Cinner”), and the location 1218 is stored andthereby designated as the outer edge of the wall (e.g., “Couter”). Inthe rules described above with reference to FIGS. 10 and 11, Scenario Ican be implemented via an execution path including 1032, 1042, 1046, and1058.

In the example of 12B (“Scenario III”), the location 1226 is stored andthereby designated as the inner edge of the wall (e.g., “Cinner”), andthe location 1228 is stored and thereby designated as the outer edge ofthe wall (e.g., “Couter”). In the rules described above with referenceto FIGS. 10 and 11, Scenario III can be implemented via an executionpath including 1032, 1062, 1072, and 1074.

In the example of 12C (“Scenario IIA”), the location 1236 is stored andthereby designated as the inner edge of the wall (e.g., “Cinner”), andthe location 1238 is stored and thereby designated as the outer edge ofthe wall (e.g., “Couter”). In the rules described above with referenceto FIGS. 10 and 11, Scenario IIA can be implemented via an executionpath including 1032, 1062, and 1066.

In the example of 12D, (“Scenario IIB”) the location 1246 is stored andthereby designated as the inner edge of the wall (e.g., “Cinner”), andthe location 1248 is stored and thereby designated as the outer edge ofthe wall (e.g., “Couter”). In the rules described above with referenceto FIGS. 10 and 11, Scenario IIB can be implemented via an executionpath including 1032, 1042, and 1056.

In the example of 13A (“Scenario V”), the wall thickness is determinedto be zero (0). The location of the anomaly (e.g., C2) is designated asthe location 1317 of the maximum value on the curve (e.g. “Cmax”) underthe rules for the scenario. In the rules described above with referenceto FIGS. 10 and 11, Scenario V can be implemented via an execution pathincluding 1012 and 1016.

In the example of 13B (“Scenario IVA”), the location 1326 is stored andthereby designated as the inner edge of the of the wall (e.g.,“Cinner”), and the location 1328 is stored and thereby designated as theouter edge of the wall (e.g., “Couter”). In the rules described abovewith reference to FIGS. 10 and 11, Scenario IVA can be implemented viaan execution path including 1012, 1022, and 1024.

In the example of 13C (“Scenario IVB”), the location 1336 is stored andthereby designated as the inner edge of the wall (e.g., “Cinner”), andthe location 1338 is stored and thereby designated as the outer edge ofthe wall (e.g., “Couter”). In the example, the location 1337 of themaximum value on the curve under the rules for the scenario (“Cmax”) isused to find the location 1338 of the outer edge of the wall (“Couter”).In the rules described above with reference to FIGS. 10 and 11, ScenarioIVB can be implemented via an execution path including 1032, 1062, 1064,1072, and 1082.

As is shown in the graphs, a variety of scenarios can be dealt witheffectively. Given an anomaly location, the rules can calculate the wallthickness, even if it is not known a priori whether the anomaly locationis in air or within the wall. Further the described rules are able tomeasure wall thickness, taking into account the presence of fat layersor other structures in the digital representation of the anatomicalstructure.

Details of Exemplary Implementation Normalizing Wall Thickness

Wall thickness related to a candidate anomaly is particularly ofinterest. The various calculations above can be done for an anomaly,based on an initial estimate of the anomaly's location as determined bydetecting regions with elliptical curvature in a digital representationof an anatomical structure.

Wall thickness can be normalized as described in the formula (I), above.The calculated normalized wall thickness can be used for a variety ofpurposes. For example, wall thickness can be used to colorize thesurface of an anatomical structure (e.g., those regions proximatecandidate anomalies of interest) when presented for review by thephysician. For instance, thresholds for one or two standard deviationsabove regional wall thickness can be used for indicating the possiblepresence of an abnormality.

The normalized wall thickness can also be used as an input to aclassifier for distinguishing true positive from false positivecandidate anomalies.

Wall thickness atlases can be represented in a database of normal wallthickness and Z-score tolerances. For example, for colon analysis, atable of values of normal wall thickness as a function of distension andlocation of the colon can be prepared by using normal colons. The tablecan be developed for different genders and age ranges. Also, it can benormalized to body habitus using body surface area or other measures ofpatients' size. Distensions may be computed using regional lumenvolumetric measurements with or without normalization for unit length.

There are a number of ways to calculate regional wall thickness. One wayis to identify the polyp neck and then to sample the wall thickness inan annulus surrounding the polyp neck. The annulus may haveuser-adjustable or a fixed radius. The annulus may be immediately at thepolyp neck or may be set off from it by a fixed or user-adjustableradius. The radius of the annulus and the offset from the polyp neck canbe set by the user, can be fixed, or can be a function of distension,colonic location, or other relevant parameter, for example, size ofpotential detection.

Typically, regional wall thickness measurements are most helpful whenmade away from sites of pathology (e.g., away from anomalies).Therefore, regional wall measurements can be restricted to points alongthe wall or at an approximating isosurface that have nonpolypoid shape.For example, measurements can be restricted to regions having curvatureof elliptical pit, planar, cylindrical, or hyperbolic types.

Further, it is possible to detect whether regional wall thicknessmeasurements are being made on a haustral fold. Such a determination canbe fed to a classifier, and classification can be based on such adetermination.

Exemplary Implementation Normalized Wall Thickness

A set of over 24,000 anomalies known to be false positives and 38anomalies known to be true positives in colon-related data were analyzedby measuring normalized wall thickness related to the anomaly (e.g.,candidate polyp). The area under the ROC curve for normalized wallthickness was 0.64. True positive detections were twice as likely tohave a normalized wall thickness greater than zero compared to falsepositives (58% vs. 30%). Thresholds for the Z-score (e.g., zero) can bechosen dynamically or by using neural networks.

Details of Exemplary Implementation Template Matching

Another technique useful for classifying anomalies is template matching.In general, a detected anomaly can be matched against a digital templateto find similarity. The measurement of similarity (e.g., a similaritycoefficient) can be used for classification. Such templates can be builtfrom images of actual scans (e.g., parallel CT slices with anomalies ofinterest) or by idealized anomalies of interest. For example,hemisphere, round plateau, and half cylinder/classic polyp templates canbe created.

An exemplary method 1400 for template matching is shown in FIG. 14. Themethod 1400 can be performed by software. At 1422, a template isselected. As explained below, plural templates can be provided.

At 1442, a candidate anomaly of interest is scored via the template. Forexample, the template can be shifted, rotated, sized, scaled, sheared,or any combination of these in an attempt to fit it to the candidateanomaly. A similarity score is calculated.

At 1452, if the candidate anomaly has a score below a predetermined oradjustable threshold, the anomaly is rejected (e.g., denoted as not ofinterest). Alternatively, the score can be fed to a classifier, whichconsiders at least the score when classifying the candidate anomaly.

Template matching can be performed in two or three dimensions. In twodimensions, the templates may be round, elliptical, square, orrectangular, varying in size from several pixels to several dozen pixels(or more), the size being fixed or variable. For example, desirablefixed dimensions are square templates that are a power of two in size.Three-dimensional templates may be isotropic or anisotropic in voxeldimension.

The templates themselves can be small images or volumes and may have anybit depth (e.g., eight, twelve, or sixteen bits). An infinite variety oftemplates (e.g., objects represented by the templates) are available(e.g., round, oval, spherical, or ellipsoidal shapes, sessile orpedunculated shapes, lobulated shapes or masses). Where the template isnot radially symmetric, it may be desirable to orient the templateaccording to the direction of the surface normal corresponding to thedetection. Thus, a template for a sessile polyp, for example, could beoriented to match the position of the colonic wall. In addition, thetemplate may vary as to size of the polyp, for example, radius, width ofthe transition from polyp to base, or angle of the polyp neck. Thetemplates may seek to locate lucencies between the head of a polyp andits attachment to the colonic wall, for example to identify the locationof the stalk for a pedunculated polyp.

While seeking to match the template against the candidate anomaly, thetemplate may be shifted on a voxel by voxel basis to determine themaximum similarity.

Similarity may be computed using a variety of functions, includingcross-correlation measures. For example, the following formula can beused:

$\begin{matrix}{{{{Sim}\left( {a,b} \right)} = \frac{\sum\limits_{i = 0}^{n - 1}{\left( {a_{i} - \overset{\_}{a}} \right)\left( {b_{i} - \overset{\_}{b}} \right)}}{\sqrt{\sum\limits_{i = 0}^{n - 1}\left( {a_{i} - \overset{\_}{a}} \right)^{2}}\sqrt{\sum\limits_{i = 0}^{n - 1}\left( {b_{i} - \overset{\_}{b}} \right)^{2}}}}{where}} & (3) \\{{\overset{\_}{a} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}a_{i}}}},{\overset{\_}{b} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}b_{i}}}}} & \left( {4,5} \right)\end{matrix}$“a” is for the image, and “b” is the template; “n” is the number ofpixels or voxels. Further information can be found in Lee et al.,“Automated Detection of Pulmonary Nodules in Helical CT Images Based onan Improved Template-Matching Technique,” IEEE Transactions on MedicalImaging, Vol. 20, No. 7, pages 595-604 (July 2001).

The cross-correlation coefficient ranges from −1 to +1. The similarityfunctions need not depend on the actual intensity values, or they candepend on whether an intensity is in a particular range as denoted inthe template. In this way, the shape of the intensity value distributionin the template and the candidate anomaly can be matched without regardto particular intensity values.

The similarity scores can be used for classification via a rule-basedthreshold or input to a more complex classifier such as a neural networkor a genetic algorithm. As a result of template matching, coordinates ofthe center of the anomaly, its radius, its orientation, itscross-sectional area, its volume, or any combination of such values canbe produced.

A shift between the template and image can be used, for example, if twoseries x(i) and y(i) are considered where i=0, 1, 2 . . . N−1. Then,cross-correlation r at shift d is defined as

$\begin{matrix}{{r(d)} = \frac{\sum\limits_{i}\left\lbrack {\left( {{x(i)} - {mx}} \right)*\left( {{y\left( {i - d} \right)} - {my}} \right)} \right\rbrack}{\sqrt{\sum\limits_{i}\left( {{x(i)} - {mx}} \right)^{2}}\sqrt{\sum\limits_{i}\left( {{y\left( {i - d} \right)} - {my}} \right)^{2}}}} & (6)\end{matrix}$where mx and my are the means of the corresponding series.

If the above is computed for shifts d=−(N−1), . . . , −1, 0, 1, 2, . . ., N−1, then it results in a cross-correlation series of twice the lengthof the original series. Such functions can easily be extended to two orthree dimensions.

Details of Exemplary Implementation Exemplary Templates

The templates can be constructed to be various shapes (e.g., 3D-grid)and sizes (e.g., 32×32 pixels). The templates can include features suchas radius, slope, center, and general shape description. A similaritymeasure can be computed using the cross-correlation coefficient betweenthe template and candidate anomalies (e.g., image slices of theanomalies). Plural similarity measures can be taken by varying thefeatures within set of adjustable ranges. The plural similarity measurescan be processed, for example, by storing the maximum value ofsimilarity and at what template position such similarity was measured.The various features of the template, including radius and translationcan also be stored.

Exemplary templates are shown in FIGS. 15-17. In the example, polypmodels were generated with reference to idealized anomalies of interest.The 2D images are displayed as 3D surfaces where 2 dimensions (X and Y)stand for the position of the model pixel in the polyp model image andthe third dimension stands for the image function (e.g., imageintensity) of the respective pixel.

FIG. 15 depicts a template being a hemisphere model. FIG. 16 depicts atemplate being a round plateau model. FIG. 17 depicts a template being aclassic polyp model (e.g., a polyp seen in profile). Such idealizedtemplates can be constructed via a number of software packages.

Details of Exemplary Implementation Rectal Tube Removal

Template matching can also be used for an entirely different purpose:rectal tube removal. An exemplary template shown in FIG. 18 is formatching to a rectal tube and can be used to detect the position of arectal tube and remove it from the CT images sequence.

Similarly, a template can be constructed by which any region not to beconsidered can be removed from consideration. In the example, the widthof the wall of the rectal tube is a parameter that can be varied.

Exemplary Implementation Template Matching

A set of over 24,000 anomalies known to be false positives and 38anomalies known to be true positives in colon-related data were analyzedby performing template matching via a truncated cone to detect classicround polyps. The area under the ROC curve for template similarity was0.63.

Details of Exemplary Implementation Other Characteristics

Based on the technologies described herein, a variety of characteristicscan be measured for use by a software classifier. Exemplarycharacteristics are shown in Table 1. Any combination of thecharacteristics can be fed to a software classifier. In the example, adata structure can be constructed to represent a curved, folded surfacein three-dimensional space via triangles. Nearest neighbors can be foundby navigating among the triangles. A neighborhood of triangle verticescan be defined, for example, as those neighboring (e.g., connected)vertices passing a curvature test (e.g., elliptical curvature of thepeak type).

TABLE 1 Characteristics Characteristic Description LesionVertices numberof vertices forming neighborhood Lesion Size distance in cm between twomost separated vertices from surface neighborhood Lesion Sphericity 2 *|LesionMaxAvgCurv − LesionMinAvgCurv|/ (LesionMaxAvgCurv +LesionMinAvgCurv) LesionGaussianAvgCurv (Gaussian Average Curvature) −Gauss Curv averaged over all vertices from surface neighborhoodLesionMeanAvgCurv (Mean Average Curvature) − Mean Curv averaged over allvertices from surface neighborhood LesionMinAvgCurv (Min AverageCurvature) − Min Curv averaged over all vertices from surfaceneighborhood LesionMaxAvgCurv (Max Average Curvature) − Max Curvaveraged over all vertices from surface neighborhood curv.min =curv.mean − sqrt(curv.mean ** 2 − curv.gauss); curv.max = curv.mean +sqrt(curv.mean ** 2 − curv.gauss); PrincCurvDiff sqrt(LesionMeanAvgCurv** 2 − LesionGaussianAvgCurv) LesionGaussianSDCurv std of vertices fromsurface neighborhood LesionMeanSDCurv std of vertices from surfaceneighborhood LesionMinSDCurv std of vertices from surface neighborhoodLesionMaxSDCurv std of vertices from surface neighborhoodLesionCentroidX,Y,Z mean over positions of vertices from surfaceneighborhood LesionCentrodRow,Column, int indexes based onLesionCentroidX,Y,Z to nearest Slice node in lattice NewSphericitysphericity averaged over vertices' sphericities from surfaceneighborhood SDNewSphericity std of NewSphericitys from surfaceneighborhood CentroidWD maxIntensity along ray given by mean normal(NormalX,Y,Z) - set of flexible rules chosen to determine where on rayto stop and get intensity RegionDensity averaged intensity over 12points from diamond shaped region, centered at the same point whereCentroidWD is calculated SD RegionDensity std from diamond shapedregion, defined as above WallThick measured along ray (as CentroidWD),“maxIntensity - 50” rule applies to get wall thicknessGauss,Mean,Min,Max, 3^(rd) order moment of given curvature/sphericityNewSphericityCurvSkew statistics (build for vertices forming surfaceneighborhood); Gauss,Mean,Min,Max, the same but 4^(th) orderNewSphericityCurvKurt Area (in sq mm) of all full triangles constitutingsurface neighborhood, Perimeter total length (in cm) of island's shoreline but excluding pieces like peninsula in the neighborhoodLenNonTriangl total length (in cm) of pieces like peninsula, bridge onthe lake NumNonTriangl total number of pieces like peninsula, bridge onthe lake MultiLinks nonzero value signals topological anomaly TwoLinksnumber of segments which are part of any full triangle from surfaceneighborhood OneLinks tot number of segments which are part of at mostone triangle (so, peninsula like, bridges, etc. are counted here);NumTriangl total number of full triangles constituting surfaceneighborhood Similarity correlation coefficient between a 2D image (32 ×32) of polyp and a model (template - like semisphere with somemodifications) AspectRatio width/length of bounding box containing allvertices from surface neighborhood projected onto a plane perpendicularto NormalX,Y,Z ModelRad,Xshiftt,Yshift parameters of model for whichSimilarity is max (for currently used model they are radius ofsemisphere and shift of semisphere center with respect to the center of32 × 32 polyp image) LesionCntr2X,Y,Z second centroid based on rulesused in computing CentroidWD (midpoint between outer and inner edge ofwall) LesionCntr2Row,Column, int indexes based on WallThick2 to nearestnode in Slice lattice WallThick2 second wall thickness based on rulesused in computing CentroidWD (distance between inner edge and outer edgeof wall) MeanLocWallThick2 reported only when - N option is set and Neckneighborhood is build, it works like this: apply procedure used tocalculate WallThick2 for every vertex on surface which is within certainregion (like ring, centred around polyp's neck) and find mean and std(see StdLocWallThick2 below) of resulting wall thickness2StdLocWallThick2 see MeanLocWallThick2 NormedWallTh2 (WallThick2 −MeanLocWallThick2)/ StdLocWallThick2 Compactness Area/Perimeter ** 2VolCurvConnect volume (in cc) of connected voxels within a given cubearound polyp which passed curvature criteria - be aware that a conceptof ‘connectivity’ depends on the value of a flag a) 0 (default for flag)takes the largest segment of hard connected voxels with acceptedcurvature within a cube; b) 1 takes voxels with accepted curvaturewithin a cube; c) 2 takes voxels with accepted curvature which areconnected via a mask within a cube VolCurvMass total mass of connectedvoxels (i.e. sum of intensity) VolCurvDens VolCurvMass/number ofconnected voxels (so, it's mass per voxel) VolCurvGauss_Avg,_Std averageand std of gauss curvatures determined from connected voxelsVolCurvMean_Avg,_Std as above for mean curv VolCurvMax_Avg,_Std as abovebut for max curv VolCurvMin_Avg,_Std as above but for min curvNormalX,Y,Z mean normal, averaged over all normals from surfaceneighborhood CmassCMass_X,_Y,_Z [sum over i: m_i *(x,y,z)_i]/VolCurvMass where sum is over all connected voxels and m_i isintensity of a given voxel; RadiusInertia calculated with respect toaxes coming through CmassCMass and LesionCentroid - radius of inertia Ris defined as TotMass * R ** 2 = {sum over i} m_i * r_i ** 2

For CentroidWD, there are at least five possible scenarios which dependon maxIntensity and 3 constants: INNER_WALL_THRESHOLD=−500 HU,LOW_FAT_THRESHOLD=−100 HU, HIGH_FAT_THRESHOLD=−40 HU. The scenarios areas follows:

TABLE 2 Scenarios Scenario Description I (e.g., FIG. 12A)LOW_FAT_THRESHOLD < maxIntensity < HIGH_FAT_THRESHOLD, at least twosubsequent points are within fat - a sign for flat plateau II (e.g.,FIGS. 12C maxIntensity > HIGH_FAT_THRESHOLD and 12D) III (e.g., FIG.12B) LOW_FAT_THRESHOLD < maxIntensity < HIGH_FAT_THRESHOLD, no flatplateau observed IV (e.g., FIG. 13B INNER_WALL_THRESHOLD < maxIntensity< and 13C) LOW_FAT_THRESHOLD V (e.g., FIG. 13A) maxIntensity <INNER_WALL_THRESHOLD

In FIGS. 12 and 13, C2=LesionCntr2X,Y,Z (2^(nd) centroid) andWT2=WallThick2. Zero on horizontal axes corresponds toLesionCentroidX,Y,Z (1^(st) centroid) while intensity profile (verticalaxes) is shown along ray=NormalX,Y,Z. Point corresponding tomaxIntensity has (e.g., always) positive x coordinate (although ultimatemax found along ray may be found higher for negative x coordinate—seee.g. FIG. 12D “indirect case”).

Details of Exemplary Implementation Classifier

As described above, the classifier can be chosen from a variety ofarchitectures and include any combination of the describedcharacteristics. For example, a classifier can be built as a neuralnetwork and trained on neck characteristics, normalized wall thickness,and template matching measurements. After training, the neural networkcan be tested and then deployed for use.

Details of Exemplary Implementation Presenting Results

FIG. 19 shows an exemplary graphical depiction 1900 of a portion of avirtual anatomical structure. In a presented user interface that can beused in conjunction with any of the technologies described herein, thedepiction 1900 includes colored areas (e.g., regions) to assist indetection of anomalies of interest (e.g., polyps).

In the example depiction 1900, areas 1902A, 1902B, and 1902C can appearin a first color (e.g., yellow) to denote regions of ellipticalcurvature of the pit subtype. The areas 1908A, 1908B, and 1908C canappear in a second color (e.g., green) to denote regions of hyperboliccurvature (e.g., possible necks of anomalies). The areas 1932A and 1932Bcan appear in a third color (e.g., orange) to indicate ellipticalcurvature of the peak subtype (e.g., likely anomaly sites).

Exemplary Computer System for Conducting Analysis

FIG. 20 and the following discussion provide a brief, generaldescription of a suitable computing environment for the software (e.g.,computer programs) described above. The methods described above can beimplemented in computer-executable instructions organized in programmodules. The program modules include the routines, programs, objects,components, and data structures that perform the tasks and implement thedata types for implementing the techniques described above.

While FIG. 20 shows a typical configuration of a desktop computer, theinvention may be implemented in other computer system configurations,including multiprocessor systems, microprocessor-based or programmableconsumer electronics, minicomputers, mainframe computers, and the like.The invention may also be used in distributed computing environmentswhere tasks are performed in parallel by processing devices to enhanceperformance. For example, tasks related to measuring characteristics ofcandidate anomalies can be performed simultaneously on multiplecomputers, multiple processors in a single computer, or both. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

The computer system shown in FIG. 20 is suitable for implementing thetechnologies described herein and includes a computer 2020, with aprocessing unit 2021, a system memory 2022, and a system bus 2023 thatinterconnects various system components, including the system memory tothe processing unit 2021. The system bus may comprise any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using a bus architecture. The systemmemory includes read only memory (ROM) 2024 and random access memory(RAM) 2025. A nonvolatile system (e.g., BIOS) can be stored in ROM 2024and contains the basic routines for transferring information betweenelements within the personal computer 2020, such as during start-up. Thepersonal computer 2020 can further include a hard disk drive 2027, amagnetic disk drive 2028, e.g., to read from or write to a removabledisk 2029, and an optical disk drive 2030, e.g., for reading a CD-ROMdisk 2031 or to read from or write to other optical media. The hard diskdrive 2027, magnetic disk drive 2028, and optical disk drive 2030 areconnected to the system bus 2023 by a hard disk drive interface 2032, amagnetic disk drive interface 2033, and an optical drive interface 2034,respectively. The drives and their associated computer-readable mediaprovide nonvolatile storage of data, data structures,computer-executable instructions (including program code such as dynamiclink libraries and executable files), and the like for the personalcomputer 2020. Although the description of computer-readable media aboverefers to a hard disk, a removable magnetic disk, and a CD, it can alsoinclude other types of media that are readable by a computer, such asmagnetic cassettes, flash memory cards, digital video disks, and thelike.

A number of program modules may be stored in the drives and RAM 2025,including an operating system 2035, one or more application programs2036, other program modules 2037, and program data 2038. A user mayenter commands and information into the personal computer 2020 through akeyboard 2040 and pointing device, such as a mouse 2042. Other inputdevices (not shown) may include a microphone, joystick, game pad,satellite dish, scanner, or the like. These and other input devices areoften connected to the processing unit 2021 through a serial portinterface 2046 that is coupled to the system bus, but may be connectedby other interfaces, such as a parallel port, game port, or a universalserial bus (USB). A monitor 2047 or other type of display device is alsoconnected to the system bus 2023 via an interface, such as a displaycontroller or video adapter 2048. In addition to the monitor, personalcomputers typically include other peripheral output devices (not shown),such as speakers and printers.

The above computer system is provided merely as an example. Thetechnologies can be implemented in a wide variety of otherconfigurations. Further, a wide variety of approaches for collecting andanalyzing data related to processing candidate anomalies is possible.For example, the data can be collected, characteristics measured,anomalies classified, and the results presented on different computersystems as appropriate. In addition, various software aspects can beimplemented in hardware, and vice versa.

Alternatives

Having illustrated and described the principles of the invention inexemplary embodiments, it should be apparent to those skilled in the artthat the described examples are illustrative embodiments and can bemodified in arrangement and detail without departing from suchprinciples.

Although some of the examples describe colonography and detecting polypsin a virtual colon, the technologies can be applied to other anatomicalstructures as well. For example implementations can be applied to thebronchus, blood vessels, bladder, urinary tract, billiary tract,cerebrospinal spinal fluid containing spaces of the brain, paranasalsinuses, chambers of the heart, and the like.

In any of the methods described herein, processing can be performed bysoftware in an automated system without intervention by a user. However,user intervention may be desired in some cases, such as to adjustparameters or consider results.

In view of the many possible embodiments to which the principles of theinvention may be applied, it should be understood that the illustrativeembodiments are intended to teach these principles and are not intendedto be a limitation on the scope of the invention. We therefore claim asour invention all that comes within the scope and spirit of thefollowing claims and their equivalents.

1. A computer-implemented method for processing an anomaly in a colon,the method comprising: in a digital representation of the coloncomprising a representation of the anomaly, matching a digital templateto the anomaly; as a result of the matching, producing a valueindicating a cross-sectional area of the anomaly or a volume of theanomaly; based at least on the matching and the value indicating across-sectional area of the anomaly or the volume of the anomaly,classifying the anomaly.
 2. The method of claim 1 wherein the digitaltemplate represents a hemispherical idealized anomaly of interest. 3.The method of claim 1 wherein the digital template represents a roundplateau idealized anomaly of interest.
 4. The method of claim 1 whereinthe digital template represents an idealized classic polyp model.
 5. Themethod of claim 1 wherein the matching generates a similaritycoefficient.
 6. The method of claim 1 further comprising: as a result ofthe matching, determining a center of the anomaly.
 7. The method ofclaim 1 further comprising: as a result of the matching, determining aradius of the anomaly.
 8. The method of claim 1 further comprising: as aresult of the matching, determining an orientation of the anomaly. 9.The method of claim 1 wherein the producing produces a value indicatinga cross-sectional area of the anomaly.
 10. The method of claim 1 whereinthe producing produces a value indicating a volume of the anomaly. 11.The method of claim 1 wherein matching comprises: shifting the digitaltemplate to generate a cross-correlation series.
 12. Acomputer-implemented method of detecting anomalies of interest in avirtual anatomical structure, the method comprising: in a suitablyprogrammed computer, matching a template representing a rectal tube to aregion of the virtual anatomical structure; and based at least on thematching, eliminating the region from consideration.
 13. A classifierfor classifying a candidate anomaly of interest in a virtual anatomicalstructure, the classifier comprising: means for calculating at least onecharacteristic chosen from the group consisting of the followingcharacteristics associated with the anomaly: a characteristic of a neckof the anomaly, a normalized wall thickness of the anomaly,cross-sectional area of the anomaly based on similarity between theanomaly and a digital template, volume of the anomaly based onsimilarity between the anomaly and a digital template; and means forclassifying the anomaly based on the at least one characteristic. 14.The classifier of claim 13 wherein the means for classifying comprises aneural network.
 15. One or more non-transitory computer-readable mediahaving encoded thereon computer-executable instructions for performing amethod for processing an anomaly in a colon, the method comprising: in adigital representation of the colon comprising a representation of theanomaly, matching a digital template to the anomaly, wherein the digitaltemplate denotes a particular range of intensity values and matchingdepends on whether an intensity of a pixel or voxel is within theparticular range of intensity values; based at least on the matching,classifying the anomaly.
 16. The one or more computer-readable media ofclaim 15 wherein the digital template represents a hemisphericalidealized anomaly of interest.
 17. The one or more computer-readablemedia of claim 15 wherein the digital template represents a roundplateau idealized anomaly of interest.
 18. The one or morecomputer-readable media of claim 15 wherein the digital templaterepresents an idealized classic polyp model.
 19. The one or morecomputer-readable media of claim 15 wherein the matching generates asimilarity coefficient.
 20. The one or more computer-readable media ofclaim 15 wherein the method further comprises: as a result of thematching, determining a center of the anomaly.
 21. The one or morecomputer-readable media of claim 15 wherein the method furthercomprises: as a result of the matching, determining a radius of theanomaly.
 22. The one or more computer-readable media of claim 15 whereinthe method further comprises: as a result of the matching, determiningan orientation of the anomaly.
 23. The one or more computer-readablemedia of claim 15 wherein the method further comprises: as a result ofthe matching, determining a cross-sectional area of the anomaly.
 24. Theone or more computer-readable media of claim 15 wherein the methodfurther comprises: as a result of the matching, determining a volume ofthe anomaly.
 25. The one or more computer-readable media of claim 15wherein the method further comprises: shifting the digital template togenerate a cross-correlation series.
 26. A machine comprising: one ormore microprocessors coupled to memory; wherein the one or moremicroprocessors are programmed to process a digital representation of ananomaly in a colon by: in a digital representation of the coloncomprising the digital representation of the anomaly, matching a digitaltemplate to the digital representation of the anomaly, wherein thematching generates a similarity score, the digital template denotes aparticular range of intensity values, matching depends on whether anintensity is within the particular range of intensity values, and thematching matches without regard to particular intensity values; andbased at least on the matching, classifying the digital representationof the anomaly, wherein the classifying comprises feeding the similarityscore to a classifier, which considers at least the similarity scorewhen classifying the digital representation of the anomaly; wherein thedigital template represents a hemispherical idealized anomaly ofinterest, a round plateau idealized anomaly of interest, or an idealizedclassic polyp model.
 27. The one or more computer-readable media ofclaim 15 wherein a shape of an intensity value distribution in thetemplate and the anomaly are matched without regard to particularintensity values.